


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


A_     :       str   							=logging.getLogger(__name__)





@dataclass
class 							lowercase_					:






							"""simple docstring"""







							snake_case_										=		field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
							snake_case_										=		field(
							    default=__snake_case      ,metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
							snake_case_										=		field(
							    default=__snake_case      ,metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
							snake_case_										=		field(
							    default=__snake_case      ,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}      ,)
							snake_case_										=		field(default=__snake_case      ,metadata={'''help''': '''Whether tp freeze the encoder.'''})
							snake_case_										=		field(default=__snake_case      ,metadata={'''help''': '''Whether  to freeze the embeddings.'''})





@dataclass
class 							lowercase_					:






							"""simple docstring"""







							snake_case_										=		field(
							    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''})
							snake_case_										=		field(
							    default='''summarization'''      ,metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}      ,)
							snake_case_										=		field(
							    default=10_24      ,metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }      ,)
							snake_case_										=		field(
							    default=1_28      ,metadata={
							        '''help''': (
							            '''The maximum total sequence length for target text after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }      ,)
							snake_case_										=		field(
							    default=1_42      ,metadata={
							        '''help''': (
							            '''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded. '''
							            '''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
							            '''during ``evaluate`` and ``predict``.'''
							        )
							    }      ,)
							snake_case_										=		field(
							    default=1_42      ,metadata={
							        '''help''': (
							            '''The maximum total sequence length for test target text after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }      ,)
							snake_case_										=		field(default=-1      ,metadata={'''help''': '''# training examples. -1 means use all.'''})
							snake_case_										=		field(default=-1      ,metadata={'''help''': '''# validation examples. -1 means use all.'''})
							snake_case_										=		field(default=-1      ,metadata={'''help''': '''# test examples. -1 means use all.'''})
							snake_case_										=		field(default=__snake_case      ,metadata={'''help''': '''Source language id for translation.'''})
							snake_case_										=		field(default=__snake_case      ,metadata={'''help''': '''Target language id for translation.'''})
							snake_case_										=		field(default=__snake_case      ,metadata={'''help''': '''# num_beams to use for evaluation.'''})
							snake_case_										=		field(
							    default=__snake_case      ,metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}      ,)




def 	lowerCamelCase_							(       UpperCAmelCase__							,     UpperCAmelCase__							,     UpperCAmelCase__   ):



						"""simple docstring"""




						logger.info(F"***** {split} metrics *****"   )
						for key in sorted(metrics.keys()   ):
												logger.info(F"  {key} = {metrics[key]}"   )
						save_json(_lowerCamelCase							,     os.path.join(_lowerCamelCase							,     F"{split}_results.json"   )   )




def 	lowerCamelCase_							(       ):



						"""simple docstring"""




						a_		      =		HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

						if len(sys.argv   ) == 2 and sys.argv[1].endswith(""".json"""   ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												a_      , a_      , a_		      =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
						else:
												a_      , a_      , a_		      =		parser.parse_args_into_dataclasses()

						check_output_dir(_lowerCamelCase   )

						# Setup logging
						logging.basicConfig(
						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,     datefmt="""%m/%d/%Y %H:%M:%S"""							,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,     )
						logger.warning(
						    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""							,     training_args.local_rank							,     training_args.device							,     training_args.n_gpu							,     bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   )							,     training_args.fpaa							,     )
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank   ):
												transformers.utils.logging.set_verbosity_info()
						logger.info("""Training/evaluation parameters %s"""							,     _lowerCamelCase   )

						# Set seed
						set_seed(training_args.seed   )

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						a_		      =		AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,     cache_dir=model_args.cache_dir							,     )

						a_		      =		("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
						for p in extra_model_params:
												if getattr(_lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase   ):
																		assert hasattr(_lowerCamelCase							,     _lowerCamelCase   ), F"({config.__class__.__name__}) doesn\'t have a `{p}` attribute"
																		setattr(_lowerCamelCase							,     _lowerCamelCase							,     getattr(_lowerCamelCase							,     _lowerCamelCase   )   )

						a_		      =		AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,     cache_dir=model_args.cache_dir							,     )
						a_		      =		AutoModelForSeqaSeqLM.from_pretrained(
						    model_args.model_name_or_path							,     from_tf=""".ckpt""" in model_args.model_name_or_path							,     config=_lowerCamelCase							,     cache_dir=model_args.cache_dir							,     )

						# use task specific params
						use_task_specific_params(_lowerCamelCase							,     data_args.task   )

						# set num_beams for evaluation
						if data_args.eval_beams is None:
												a_		      =		model.config.num_beams

						# set decoder_start_token_id for MBart
						if model.config.decoder_start_token_id is None and isinstance(_lowerCamelCase							,     (MBartTokenizer, MBartTokenizerFast)   ):
												assert (
												    data_args.tgt_lang is not None and data_args.src_lang is not None
												), "mBart requires --tgt_lang and --src_lang"
												if isinstance(_lowerCamelCase							,     _lowerCamelCase   ):
																		a_		      =		tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		a_		      =		tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

						if model_args.freeze_embeds:
												freeze_embeds(_lowerCamelCase   )
						if model_args.freeze_encoder:
												freeze_params(model.get_encoder()   )
												assert_all_frozen(model.get_encoder()   )

						a_		      =		SeqaSeqDataset

						# Get datasets
						a_		      =		(
						    dataset_class(
						        _lowerCamelCase							,     type_path="""train"""							,     data_dir=data_args.data_dir							,     n_obs=data_args.n_train							,     max_target_length=data_args.max_target_length							,     max_source_length=data_args.max_source_length							,     prefix=model.config.prefix or """"""							,     )
						    if training_args.do_train
						    else None
						)
						a_		      =		(
						    dataset_class(
						        _lowerCamelCase							,     type_path="""val"""							,     data_dir=data_args.data_dir							,     n_obs=data_args.n_val							,     max_target_length=data_args.val_max_target_length							,     max_source_length=data_args.max_source_length							,     prefix=model.config.prefix or """"""							,     )
						    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
						    else None
						)
						a_		      =		(
						    dataset_class(
						        _lowerCamelCase							,     type_path="""test"""							,     data_dir=data_args.data_dir							,     n_obs=data_args.n_test							,     max_target_length=data_args.test_max_target_length							,     max_source_length=data_args.max_source_length							,     prefix=model.config.prefix or """"""							,     )
						    if training_args.do_predict
						    else None
						)

						# Initialize our Trainer
						a_		      =		(
						    build_compute_metrics_fn(data_args.task							,     _lowerCamelCase   ) if training_args.predict_with_generate else None
						)
						a_		      =		SeqaSeqTrainer(
						    model=_lowerCamelCase							,     args=_lowerCamelCase							,     data_args=_lowerCamelCase							,     train_dataset=_lowerCamelCase							,     eval_dataset=_lowerCamelCase							,     data_collator=SeqaSeqDataCollator(
						        _lowerCamelCase							,     _lowerCamelCase							,     model.config.decoder_start_token_id							,     training_args.tpu_num_cores   )							,     compute_metrics=_lowerCamelCase							,     tokenizer=_lowerCamelCase							,     )

						a_		      =		{}
						# Training
						if training_args.do_train:
												logger.info("""*** Train ***"""   )

												a_		      =		trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
												a_		      =		train_result.metrics
												a_		      =		data_args.n_train

												trainer.save_model()  # this also saves the tokenizer

												if trainer.is_world_process_zero():
																		handle_metrics("""train"""							,     _lowerCamelCase							,     training_args.output_dir   )
																		all_metrics.update(_lowerCamelCase   )

																		# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
																		trainer.state.save_to_json(os.path.join(training_args.output_dir							,     """trainer_state.json"""   )   )

																		# For convenience, we also re-save the tokenizer to the same directory,
																		# so that you can share your model easily on huggingface.co/models =)
																		tokenizer.save_pretrained(training_args.output_dir   )

												# Evaluation
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""   )

												a_		      =		trainer.evaluate(metric_key_prefix="""val"""   )
												a_		      =		data_args.n_val
												a_		      =		round(metrics["""val_loss"""]							,     4   )

												if trainer.is_world_process_zero():
																		handle_metrics("""val"""							,     _lowerCamelCase							,     training_args.output_dir   )
																		all_metrics.update(_lowerCamelCase   )

						if training_args.do_predict:
												logger.info("""*** Predict ***"""   )

												a_		      =		trainer.predict(test_dataset=_lowerCamelCase							,     metric_key_prefix="""test"""   )
												a_		      =		test_output.metrics
												a_		      =		data_args.n_test

												if trainer.is_world_process_zero():
																		a_		      =		round(metrics["""test_loss"""]							,     4   )
																		handle_metrics("""test"""							,     _lowerCamelCase							,     training_args.output_dir   )
																		all_metrics.update(_lowerCamelCase   )

																		if training_args.predict_with_generate:
																								a_		      =		tokenizer.batch_decode(
																								    test_output.predictions							,     skip_special_tokens=_lowerCamelCase							,     clean_up_tokenization_spaces=_lowerCamelCase   )
																								a_		      =		lmap(str.strip							,     _lowerCamelCase   )
																								write_txt_file(_lowerCamelCase							,     os.path.join(training_args.output_dir							,     """test_generations.txt"""   )   )

						if trainer.is_world_process_zero():
												save_json(_lowerCamelCase							,     os.path.join(training_args.output_dir							,     """all_results.json"""   )   )

						return all_metrics




def 	lowerCamelCase_							(       UpperCAmelCase__   ):



						"""simple docstring"""




						main()


if __name__ == "__main__":
	main()







"""simple docstring"""





import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTMSNForImageClassification, ViTMSNModel
  from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class 	a		:






    def __init__(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				str=13			,		__SCREAMING_SNAKE_CASE	:				int=30			,		__SCREAMING_SNAKE_CASE	:				List[Any]=2			,		__SCREAMING_SNAKE_CASE	:				Dict=3			,		__SCREAMING_SNAKE_CASE	:				List[str]=True			,		__SCREAMING_SNAKE_CASE	:				str=True			,		__SCREAMING_SNAKE_CASE	:				Any=32			,		__SCREAMING_SNAKE_CASE	:				List[str]=5			,		__SCREAMING_SNAKE_CASE	:				List[str]=4			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=37			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]="gelu"			,		__SCREAMING_SNAKE_CASE	:				List[Any]=0.1			,		__SCREAMING_SNAKE_CASE	:				Tuple=0.1			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=10			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=0.02			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=None			,		)	->						List[str]:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		image_size
      lowerCamelCase_       	=		patch_size
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		is_training
      lowerCamelCase_       	=		use_labels
      lowerCamelCase_       	=		hidden_size
      lowerCamelCase_       	=		num_hidden_layers
      lowerCamelCase_       	=		num_attention_heads
      lowerCamelCase_       	=		intermediate_size
      lowerCamelCase_       	=		hidden_act
      lowerCamelCase_       	=		hidden_dropout_prob
      lowerCamelCase_       	=		attention_probs_dropout_prob
      lowerCamelCase_       	=		type_sequence_label_size
      lowerCamelCase_       	=		initializer_range
      lowerCamelCase_       	=		scope

      # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      lowerCamelCase_       	=		(image_size // patch_size) ** 2
      lowerCamelCase_       	=		num_patches + 1






    def   UpperCamelCase			(			self	:				Dict   )	->						Union[str, Any]:
      lowerCamelCase_       	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      lowerCamelCase_       	=		None
      if self.use_labels:
        lowerCamelCase_       	=		ids_tensor([self.batch_size]			,		self.type_sequence_label_size   )

      lowerCamelCase_       	=		self.get_config()

      return config, pixel_values, labels






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Union[str, Any]:
      return ViTMSNConfig(
          image_size=self.image_size			,		patch_size=self.patch_size			,		num_channels=self.num_channels			,		hidden_size=self.hidden_size			,		num_hidden_layers=self.num_hidden_layers			,		num_attention_heads=self.num_attention_heads			,		intermediate_size=self.intermediate_size			,		hidden_act=self.hidden_act			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		initializer_range=self.initializer_range			,		)






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				int   )	->						Any:
      lowerCamelCase_       	=		ViTMSNModel(config=__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size)   )






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Dict			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				str   )	->						List[str]:
      lowerCamelCase_       	=		self.type_sequence_label_size
      lowerCamelCase_       	=		ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE			,		labels=__SCREAMING_SNAKE_CASE   )
      print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'   )
      print('Labels: {labels}'   )
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.type_sequence_label_size)   )

      # test greyscale images
      lowerCamelCase_       	=		1
      lowerCamelCase_       	=		ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()

      lowerCamelCase_       	=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.type_sequence_label_size)   )






    def   UpperCamelCase			(			self	:				List[str]   )	->						Tuple:
      lowerCamelCase_       	=		self.prepare_config_and_inputs()
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		config_and_inputs
      lowerCamelCase_       	=		{'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class 	a		(   __snake_case    , __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	str		=					(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE		:	Dict		=					(
        {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    SCREAMING_SNAKE_CASE		:	Dict		=					False
    SCREAMING_SNAKE_CASE		:	Union[str, Any]		=					False
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					False
    SCREAMING_SNAKE_CASE		:	str		=					False






    def   UpperCamelCase			(			self	:				List[Any]   )	->						List[str]:
      lowerCamelCase_       	=		ViTMSNModelTester(self   )
      lowerCamelCase_       	=		ConfigTester(self			,		config_class=__SCREAMING_SNAKE_CASE			,		has_text_modality=__SCREAMING_SNAKE_CASE			,		hidden_size=37   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      self.config_tester.run_common_tests()






    @unittest.skip(reason='ViTMSN does not use inputs_embeds'   )
    def   UpperCamelCase			(			self	:				Tuple   )	->						Union[str, Any]:
      pass






    def   UpperCamelCase			(			self	:				List[Any]   )	->						List[Any]:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        self.assertIsInstance(model.get_input_embeddings()			,		(nn.Module)   )
        lowerCamelCase_       	=		model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE			,		nn.Linear   )   )






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Any:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		inspect.signature(model.forward   )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCamelCase_       	=		[*signature.parameters.keys()]

        lowerCamelCase_       	=		['pixel_values']
        self.assertListEqual(arg_names[:1]			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[int]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase_       	=		ViTMSNModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
        self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )







def 	lowerCamelCase__			(				) ->							List[Any]:
  lowerCamelCase_       	=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
  return image






@require_torch
@require_vision
class 	a		(   unittest.TestCase						):






    @cached_property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Any:
      return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'   ) if is_vision_available() else None






    @slow
    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      torch.manual_seed(2   )
      lowerCamelCase_       	=		ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'   ).to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.default_image_processor
      lowerCamelCase_       	=		prepare_img()
      lowerCamelCase_       	=		image_processor(images=__SCREAMING_SNAKE_CASE			,		return_tensors='pt'   ).to(__SCREAMING_SNAKE_CASE   )

      # forward pass
      with torch.no_grad():
        lowerCamelCase_       	=		model(**__SCREAMING_SNAKE_CASE   )

      # verify the logits
      lowerCamelCase_       	=		torch.Size((1, 1000)   )
      self.assertEqual(outputs.logits.shape			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.tensor([-0.0_803, -0.4_454, -0.2_375]   ).to(__SCREAMING_SNAKE_CASE   )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]			,		__SCREAMING_SNAKE_CASE			,		atol=1e-4   )   )




